Concepts

The triad is aDNA's universal organizing principle: every piece of project knowledge belongs in exactly one of three directories — what/, how/, or who/ ...

The aDNA ontology is a typed vocabulary of 14 base entity types — organized across the triad — that defines what kinds of things a project can contain. ...

An aDNA vault is not a filing cabinet — it's a knowledge graph. Files are nodes, wikilinks are edges, and AGENTS.md files are the navigation layer that ...

Every aDNA project has five ALLCAPS governance files at its root — CLAUDE.md, MANIFEST.md, STATE.md, AGENTS.md, and README.md. Together, they form the o...

Token selection is the discipline of choosing which knowledge to load into an AI agent's context window — and, critically, which knowledge to leave out....

A project can know more than any AI agent can hold in mind at once. The convergence model solves that by narrowing the knowledge in play at each stage o...

Context optimization is the practice of designing context files — the curated knowledge agents load before doing work — so they deliver maximum decision...

Big jobs are usually too big for one workflow. Lattice composition is how aDNA snaps smaller workflows together to make bigger ones — the same way a din...

aDNA is an open standard — a publicly documented specification that anyone can implement, extend, and build upon without permission or payment. The upst...

Agentic literacy is the ability to work effectively with AI agents — not just prompting them, but structuring knowledge so agents can find, understand, ...

Think of the Context Commons as a shared library of "how to teach an AI assistant your project" — like GitHub, but for agent knowledge instead of code. ...

FAIR is a simple four-question test: can someone else Find your work, Access it, Interoperate with it, and Reuse it? aDNA bakes that test into every pie...